def make_heated_time_map(sep_array, Nside, width):
print 'generating heated time map ...'
# choose points within specified range. Example plot separations greater than 5 minutes:
# indices = (sep_array[:,0]>5*60) & (sep_array[:,1]>5*60)
indices=range(sep_array.shape[0]) # all time separations
x_pts = np.log(sep_array[indices,0])
y_pts = np.log(sep_array[indices,1])
min_val = np.min([np.min(x_pts), np.min(y_pts)])
x_pts = x_pts - min_val
y_pts = y_pts - min_val
max_val = np.max([np.max(x_pts), np.max(y_pts)])
x_pts *= (Nside-1)/max_val
y_pts *= (Nside-1)/max_val
img=np.zeros((Nside,Nside))
for i in range(len(x_pts)):
img[x_pts[i],y_pts[i]] +=1
img = ndi.gaussian_filter(img,width) # apply Gaussian filter
img = np.sqrt(img) # taking the square root makes the lower values more visible
img=np.transpose(img) # needed so the orientation is the same as scatterplot
plt.imshow(img, origin='lower')
## create custom tick marks. Calculate positions of tick marks on the transformed log scale of the image array
plt.minorticks_off()
## change font, which can also now accept latex: http://matplotlib.org/users/usetex.html
plt.rc('text',usetex=True)
plt.rc('font',family='serif')
my_max = np.max([np.max(sep_array[indices,0]), np.max(sep_array[indices,1])])
my_min = np.max([np.min(sep_array[indices,0]), np.min(sep_array[indices,1])])
pure_ticks = np.array([1e-3,1,10,60*10,2*3600,1*24*3600, 7*24*3600]) # where the tick marks will be placed, in units of seconds. An additional value will be appended to the end for the max
labels = ['1 msec','1 sec','10 sec','10 min','2 hr','1 day','1 week'] # tick labels
index_lower=np.min(np.nonzero(pure_ticks >= my_min)) # index of minimum tick that is greater than or equal to the smallest time interval. This will be the first tick with a non-blank label
index_upper=np.max(np.nonzero(pure_ticks <= my_max))
ticks = pure_ticks[index_lower: index_upper + 1]
ticks = np.log(np.hstack((my_min, ticks, my_max ))) # append values to beginning and end in order to specify the limits
ticks = ticks - min_val
ticks *= (Nside-1)/(max_val)
labels= np.hstack(('',labels[index_lower:index_upper + 1],'')) # append blank labels to beginning and end
plt.xticks(ticks, labels,fontsize=16)
plt.yticks(ticks, labels,fontsize=16)
plt.xlabel('Time Before Tweet',fontsize=18)
plt.ylabel('Time After Tweet' ,fontsize=18)
plt.show()
return None
def make_time_map(times, times_tot_mins, sep_array, Ncolors):
print 'rendering normal time map ...'
## set up color list
red=Color("red")
blue=Color("blue")
color_list = list(red.range_to(blue, Ncolors)) # range of colors evenly speced on the spectrum between red and blue. Each element is a colour object
color_list = [c.hex for c in color_list] # give hex version
fig=plt.figure()
ax =fig.add_subplot(111)
plt.rc('text',usetex=True)
plt.rc('font',family='serif')
colormap = plt.cm.get_cmap('rainbow') # see color maps at http://matplotlib.org/users/colormaps.html
order=np.argsort(times_tot_mins[1:-1]) # so that the red dots are on top
# order=np.arange(1,len(times_tot_mins)-2) # dots are unsorted
# taken from http://stackoverflow.com/questions/6063876/matplotlib-colorbar-for-scatter
sc= ax.scatter(sep_array[:,0][order],sep_array[:,1][order],c=times_tot_mins[1:-1][order],vmin=0,vmax=24*60,s=25,cmap=colormap,marker='o',edgecolors='none')
color_bar=fig.colorbar(sc,ticks=[0,24*15,24*30,24*45,24*60],orientation='horizontal',shrink=0.5)
color_bar.ax.set_xticklabels(['Midnight','18:00','Noon','6:00','Midnight'])
color_bar.ax.invert_xaxis()
color_bar.ax.tick_params(labelsize=16)
ax.set_yscale('log') # logarithmic axes
ax.set_xscale('log')
plt.minorticks_off()
pure_ticks = np.array([1e-3,1,10,60*10,2*3600,1*24*3600, 7*24*3600]) # where the tick marks will be placed, in units of seconds.
labels = ['1 msec','1 sec','10 sec','10 min','2 hr','1 day','1 week'] # tick labels
max_val = np.max([np.max(sep_array[:,0]), np.max(sep_array[:,1])])
ticks = np.hstack((pure_ticks, max_val))
min_val = np.min([np.min(sep_array[:,0]), np.min(sep_array[:,1])])
plt.xticks(ticks,labels,fontsize=16)
plt.yticks(ticks,labels,fontsize=16)
plt.xlabel('Time Before Tweet',fontsize=18)
plt.ylabel('Time After Tweet',fontsize=18)
plt.xlim((min_val, max_val))
plt.ylim((min_val, max_val))
ax.set_aspect('equal')
plt.tight_layout()
plt.show()
return None
def make_heated_time_map(sep_array, Nside, width):
print 'generating heated time map ...'
# choose points within specified range. Example plot separations greater than 5 minutes:
# indices = (sep_array[:,0]>5*60) & (sep_array[:,1]>5*60)
indices = range(sep_array.shape[0]) # all time separations
x_pts = np.log(sep_array[indices, 0])
y_pts = np.log(sep_array[indices, 1])
min_val = np.min([np.min(x_pts), np.min(y_pts)])
x_pts = x_pts - min_val
y_pts = y_pts - min_val
max_val = np.max([np.max(x_pts), np.max(y_pts)])
x_pts *= (Nside - 1) / max_val
y_pts *= (Nside - 1) / max_val
img = np.zeros((Nside, Nside))
for i in range(len(x_pts)):
img[x_pts[i], y_pts[i]] += 1
img = ndi.gaussian_filter(img, width) # apply Gaussian filter
img = np.sqrt(
img) # taking the square root makes the lower values more visible
img = np.transpose(
img) # needed so the orientation is the same as scatterplot
plt.imshow(img, origin='lower')
## create custom tick marks. Calculate positions of tick marks on the transformed log scale of the image array
plt.minorticks_off()
## change font, which can also now accept latex: http://matplotlib.org/users/usetex.html
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
my_max = np.max(
[np.max(sep_array[indices, 0]),
np.max(sep_array[indices, 1])])
my_min = np.max(
[np.min(sep_array[indices, 0]),
np.min(sep_array[indices, 1])])
pure_ticks = np.array(
[1e-3, 1, 10, 60 * 10, 2 * 3600, 1 * 24 * 3600, 7 * 24 * 3600]
) # where the tick marks will be placed, in units of seconds. An additional value will be appended to the end for the max
labels = [
'1 msec', '1 sec', '10 sec', '10 min', '2 hr', '1 day', '1 week'
] # tick labels
index_lower = np.min(
np.nonzero(pure_ticks >= my_min)
) # index of minimum tick that is greater than or equal to the smallest time interval. This will be the first tick with a non-blank label
index_upper = np.max(np.nonzero(pure_ticks <= my_max))
ticks = pure_ticks[index_lower:index_upper + 1]
ticks = np.log(np.hstack(
(my_min, ticks, my_max)
)) # append values to beginning and end in order to specify the limits
ticks = ticks - min_val
ticks *= (Nside - 1) / (max_val)
labels = np.hstack(('', labels[index_lower:index_upper + 1],
'')) # append blank labels to beginning and end
plt.xticks(ticks, labels, fontsize=16)
plt.yticks(ticks, labels, fontsize=16)
plt.xlabel('Time Before Tweet', fontsize=18)
plt.ylabel('Time After Tweet', fontsize=18)
plt.show()
return None
def make_time_map(times, times_tot_mins, sep_array, Ncolors):
print 'rendering normal time map ...'
## set up color list
red = Color("red")
blue = Color("blue")
color_list = list(
red.range_to(blue, Ncolors)
) # range of colors evenly speced on the spectrum between red and blue. Each element is a colour object
color_list = [c.hex for c in color_list] # give hex version
fig = plt.figure()
ax = fig.add_subplot(111)
plt.rc('text', usetex=False)
plt.rc('font', family='serif')
colormap = plt.cm.get_cmap(
'rainbow'
) # see color maps at http://matplotlib.org/users/colormaps.html
order = np.argsort(times_tot_mins[1:-1]) # so that the red dots are on top
# order=np.arange(1,len(times_tot_mins)-2) # dots are unsorted
# taken from http://stackoverflow.com/questions/6063876/matplotlib-colorbar-for-scatter
sc = ax.scatter(sep_array[:, 0][order],
sep_array[:, 1][order],
c=times_tot_mins[1:-1][order],
vmin=0,
vmax=24 * 60,
s=25,
cmap=colormap,
marker='o',
edgecolors='none')
color_bar = fig.colorbar(sc,
ticks=[0, 24 * 15, 24 * 30, 24 * 45, 24 * 60],
orientation='horizontal',
shrink=0.5)
color_bar.ax.set_xticklabels(
['Midnight', '18:00', 'Noon', '6:00', 'Midnight'])
color_bar.ax.invert_xaxis()
color_bar.ax.tick_params(labelsize=16)
ax.set_yscale('log') # logarithmic axes
ax.set_xscale('log')
plt.minorticks_off()
pure_ticks = np.array([
1e-3, 1, 10, 60 * 10, 2 * 3600, 1 * 24 * 3600, 7 * 24 * 3600
]) # where the tick marks will be placed, in units of seconds.
labels = [
'1 msec', '1 sec', '10 sec', '10 min', '2 hr', '1 day', '1 week'
] # tick labels
max_val = np.max([np.max(sep_array[:, 0]), np.max(sep_array[:, 1])])
ticks = np.hstack((pure_ticks, max_val))
min_val = np.min([np.min(sep_array[:, 0]), np.min(sep_array[:, 1])])
plt.xticks(ticks, labels, fontsize=16)
plt.yticks(ticks, labels, fontsize=16)
plt.xlabel('Time Before Tweet', fontsize=18)
plt.ylabel('Time After Tweet', fontsize=18)
plt.xlim((min_val, max_val))
plt.ylim((min_val, max_val))
ax.set_aspect('equal')
plt.tight_layout()
plt.show()
return None
def make_heated_time_map(
sep_array,
Nside,
width,
title=""): # plot heated time map. Nothing is returned
print("generating heated time map ...")
# choose points within specified range. Example plot separations greater than 5 minutes:
# indices = (sep_array[:,0]>5*60) & (sep_array[:,1]>5*60)
indices = list(range(sep_array.shape[0])) # all time separations
x_pts = np.log(sep_array[indices, 0])
y_pts = np.log(sep_array[indices, 1])
min_val = np.min([np.min(x_pts), np.min(y_pts)])
x_pts = x_pts - min_val
y_pts = y_pts - min_val
max_val = np.max([np.max(x_pts), np.max(y_pts)])
x_pts *= (Nside - 1) / max_val
y_pts *= (Nside - 1) / max_val
img = np.zeros((Nside, Nside))
for i in range(len(x_pts)):
# print(i)
# print((int)x_pts[i])
# print(y_pts[i])
img[(int)(x_pts[i]), (int)(y_pts[i])] += 1
img = ndi.gaussian_filter(img, width) # apply Gaussian filter
img = np.sqrt(
img) # taking the square root makes the lower values more visible
img = np.transpose(
img) # needed so the orientation is the same as scatterplot
plt.imshow(img, origin="lower")
## create custom tick marks. Calculate positions of tick marks on the transformed log scale of the image array
plt.minorticks_off()
## change font, which can also now accept latex: http://matplotlib.org/users/usetex.html
plt.rc("text", usetex=False)
plt.rc("font", family="serif")
my_max = np.max(
[np.max(sep_array[indices, 0]),
np.max(sep_array[indices, 1])])
my_min = np.max(
[np.min(sep_array[indices, 0]),
np.min(sep_array[indices, 1])])
pure_ticks = np.array(
[1e-3, 1, 10, 60 * 10, 2 * 3600, 1 * 24 * 3600, 7 * 24 * 3600])
# where the tick marks will be placed, in units of seconds. An additional value will be appended to the end for the max
labels = [
"1 ms",
"1 s",
"10 s",
"10 m",
"2 h",
"1 d",
"1 w",
] # tick labels
index_lower = np.min(np.nonzero(pure_ticks >= my_min))
# index of minimum tick that is greater than or equal to the smallest time interval. This will be the first tick with a non-blank label
index_upper = np.max(np.nonzero(pure_ticks <= my_max))
# similar to index_lower, but for upperbound
ticks = pure_ticks[index_lower:index_upper + 1]
ticks = np.log(np.hstack(
(my_min, ticks, my_max)
)) # append values to beginning and end in order to specify the limits
ticks = ticks - min_val
ticks *= (Nside - 1) / (max_val)
labels = np.hstack(("", labels[index_lower:index_upper + 1],
"")) # append blank labels to beginning and end
plt.xticks(ticks, labels, fontsize=16)
plt.yticks(ticks, labels, fontsize=16)
plt.xlabel("Time Before Tweet", fontsize=18)
plt.ylabel("Time After Tweet", fontsize=18)
plt.title(title)
plt.show()
return None
def make_time_map(
times, times_tot_mins, sep_array, Ncolors,
title): # plot standard, scatter-plot time map. Nothing is returned
print("rendering normal time map ...")
## set up color list
red = Color("red")
blue = Color("blue")
color_list = list(
red.range_to(blue, Ncolors)
) # range of colors evenly speced on the spectrum between red and blue. Each element is a colour object
color_list = [c.hex for c in color_list] # give hex version
fig = plt.figure()
ax = fig.add_subplot(111)
plt.rc("text", usetex=False)
plt.rc("font", family="serif")
colormap = plt.cm.get_cmap(
"rainbow"
) # see color maps at http://matplotlib.org/users/colormaps.html
order = np.argsort(times_tot_mins[1:-1]) # so that the red dots are on top
# order=np.arange(1,len(times_tot_mins)-2) # dots are unsorted
sc = ax.scatter(
sep_array[:, 0][order],
sep_array[:, 1][order],
c=times_tot_mins[1:-1][order],
vmin=0,
vmax=24 * 60,
s=25,
cmap=colormap,
marker="o",
edgecolors="none",
)
# taken from http://stackoverflow.com/questions/6063876/matplotlib-colorbar-for-scatter
color_bar = fig.colorbar(
sc,
ticks=[0, 24 * 15, 24 * 30, 24 * 45, 24 * 60],
orientation="horizontal",
shrink=0.5,
)
color_bar.ax.set_xticklabels(
["Midnight", "18:00", "Noon", "6:00", "Midnight"])
color_bar.ax.invert_xaxis()
color_bar.ax.tick_params(labelsize=16)
ax.set_yscale("log") # logarithmic axes
ax.set_xscale("log")
plt.minorticks_off()
pure_ticks = np.array([
1e-3, 1, 10, 60 * 10, 2 * 3600, 1 * 24 * 3600, 7 * 24 * 3600
]) # where the tick marks will be placed, in units of seconds.
labels = [
"1 ms",
"1 s",
"10 s",
"10 m",
"2 h",
"1 d",
"1 w",
] # tick labels
max_val = np.max([np.max(sep_array[:, 0]), np.max(sep_array[:, 1])])
ticks = np.hstack((pure_ticks, max_val))
min_val = np.min([np.min(sep_array[:, 0]), np.min(sep_array[:, 1])])
plt.xticks(ticks, labels, fontsize=16)
plt.yticks(ticks, labels, fontsize=16)
plt.xlabel("Time Before Tweet", fontsize=18)
plt.ylabel("Time After Tweet", fontsize=18)
plt.title(title)
plt.xlim((min_val, max_val))
plt.ylim((min_val, max_val))
ax.set_aspect("equal")
plt.tight_layout()
plt.show()